Borrow it
Coverart for item
The Resource Deep learning with Python : a hands-on introduction, Nikhil Ketkar

Deep learning with Python : a hands-on introduction, Nikhil Ketkar

Creator
Author
Summary
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production
Language
eng
Work
Publication
Extent
1 online resource
Contents
  • At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Chapter 1: Introduction to Deep Learning; Historical Context; Advances in Related Fields; Prerequisites ; Overview of Subsequent Chapters; Installing the Required Libraries ; Chapter 2: Machine Learning Fundamentals; Intuition; Binary Classification; Regression; Generalization; Regularization; Summary; Chapter 3: Feed Forward Neural Networks; Unit; Overall Structure of a Neural Network; Expressing the Neural Network in Vector Form; Evaluating the output of the Neural Network
  • Training the Neural NetworkDeriving Cost Functions using Maximum Likelihood; Binary Cross Entropy; Cross Entropy; Squared Error; Summary of Loss Functions; Types of Units/Activation Functions/Layers; Linear Unit; Sigmoid Unit; Softmax Layer; Rectified Linear Unit (ReLU); Hyperbolic Tangent; Neural Network Hands-on with AutoGrad; Summary; Chapter 4: Introduction to Theano; What is Theano; Theano Hands-On; Summary; Chapter 5: Convolutional Neural Networks; Convolution Operation; Pooling Operation; Convolution-Detector-Pooling Building Block; Convolution Variants; Intuition behind CNNs; Summary
  • Chapter 6: Recurrent Neural NetworksRNN Basics; Training RNNs; Bidirectional RNNs; Gradient Explosion and Vanishing; Gradient Clipping; Long Short Term Memory; Summary; Chapter 7: Introduction to Keras; Summary; Chapter 8: Stochastic Gradient Descent; Optimization Problems; Method of Steepest Descent; Batch, Stochastic (Single and Mini-batch) Descent; Batch; Stochastic Single Example; Stochastic Mini-batch; Batch vs. Stochastic; Challenges with SGD; Local Minima; Saddle Points; Selecting the Learning Rate; Slow Progress in Narrow Valleys; Algorithmic Variations on SGD; Momentum
  • Nesterov Accelerated Gradient (NAS)Annealing and Learning Rate Schedules; Adagrad; RMSProp; Adadelta; Adam; Resilient Backpropagation; Equilibrated SGD; Tricks and Tips for using SGD; Preprocessing Input Data; Choice of Activation Function; Preprocessing Target Value; Initializing Parameters; Shuffling Data; Batch Normalization; Early Stopping; Gradient Noise; Parallel and Distributed SGD; Hogwild; Downpour; Hands-on SGD with Downhill; Summary; Chapter 9: Automatic Differentiation; Numerical Differentiation; Symbolic Differentiation; Automatic Differentiation Fundamentals
  • Forward/Tangent Linear ModeReverse/Cotangent/Adjoint Linear Mode; Implementation of Automatic Differentiation; Source Code Transformation; Operator Overloading; Hands-on Automatic Differentiation with Autograd; Summary; Chapter 10: Introduction to GPUs; Summary; Index
Isbn
9781484227664
Instance
Label
Deep learning with Python : a hands-on introduction
Title
Deep learning with Python
Title remainder
a hands-on introduction
Statement of responsibility
Nikhil Ketkar
Creator
Author
Subject
Genre
Language
eng
Summary
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production
Member of
Cataloging source
N$T
http://library.link/vocab/creatorName
Ketkar, Nikhil
Dewey number
005.13/3
Index
no index present
LC call number
QA76.73.P98
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/subjectName
  • Machine learning
  • Python (Computer program language)
  • Data mining
  • Computer programming
  • Programming & scripting languages: general
  • Mathematical theory of computation
  • Artificial intelligence
  • COMPUTERS
  • Data mining
  • Machine learning
  • Python (Computer program language)
Label
Deep learning with Python : a hands-on introduction, Nikhil Ketkar
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Chapter 1: Introduction to Deep Learning; Historical Context; Advances in Related Fields; Prerequisites ; Overview of Subsequent Chapters; Installing the Required Libraries ; Chapter 2: Machine Learning Fundamentals; Intuition; Binary Classification; Regression; Generalization; Regularization; Summary; Chapter 3: Feed Forward Neural Networks; Unit; Overall Structure of a Neural Network; Expressing the Neural Network in Vector Form; Evaluating the output of the Neural Network
  • Training the Neural NetworkDeriving Cost Functions using Maximum Likelihood; Binary Cross Entropy; Cross Entropy; Squared Error; Summary of Loss Functions; Types of Units/Activation Functions/Layers; Linear Unit; Sigmoid Unit; Softmax Layer; Rectified Linear Unit (ReLU); Hyperbolic Tangent; Neural Network Hands-on with AutoGrad; Summary; Chapter 4: Introduction to Theano; What is Theano; Theano Hands-On; Summary; Chapter 5: Convolutional Neural Networks; Convolution Operation; Pooling Operation; Convolution-Detector-Pooling Building Block; Convolution Variants; Intuition behind CNNs; Summary
  • Chapter 6: Recurrent Neural NetworksRNN Basics; Training RNNs; Bidirectional RNNs; Gradient Explosion and Vanishing; Gradient Clipping; Long Short Term Memory; Summary; Chapter 7: Introduction to Keras; Summary; Chapter 8: Stochastic Gradient Descent; Optimization Problems; Method of Steepest Descent; Batch, Stochastic (Single and Mini-batch) Descent; Batch; Stochastic Single Example; Stochastic Mini-batch; Batch vs. Stochastic; Challenges with SGD; Local Minima; Saddle Points; Selecting the Learning Rate; Slow Progress in Narrow Valleys; Algorithmic Variations on SGD; Momentum
  • Nesterov Accelerated Gradient (NAS)Annealing and Learning Rate Schedules; Adagrad; RMSProp; Adadelta; Adam; Resilient Backpropagation; Equilibrated SGD; Tricks and Tips for using SGD; Preprocessing Input Data; Choice of Activation Function; Preprocessing Target Value; Initializing Parameters; Shuffling Data; Batch Normalization; Early Stopping; Gradient Noise; Parallel and Distributed SGD; Hogwild; Downpour; Hands-on SGD with Downhill; Summary; Chapter 9: Automatic Differentiation; Numerical Differentiation; Symbolic Differentiation; Automatic Differentiation Fundamentals
  • Forward/Tangent Linear ModeReverse/Cotangent/Adjoint Linear Mode; Implementation of Automatic Differentiation; Source Code Transformation; Operator Overloading; Hands-on Automatic Differentiation with Autograd; Summary; Chapter 10: Introduction to GPUs; Summary; Index
Control code
982957880
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781484227664
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-1-4842-2766-4
http://library.link/vocab/ext/overdrive/overdriveId
cl0501000009
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)982957880
Label
Deep learning with Python : a hands-on introduction, Nikhil Ketkar
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Chapter 1: Introduction to Deep Learning; Historical Context; Advances in Related Fields; Prerequisites ; Overview of Subsequent Chapters; Installing the Required Libraries ; Chapter 2: Machine Learning Fundamentals; Intuition; Binary Classification; Regression; Generalization; Regularization; Summary; Chapter 3: Feed Forward Neural Networks; Unit; Overall Structure of a Neural Network; Expressing the Neural Network in Vector Form; Evaluating the output of the Neural Network
  • Training the Neural NetworkDeriving Cost Functions using Maximum Likelihood; Binary Cross Entropy; Cross Entropy; Squared Error; Summary of Loss Functions; Types of Units/Activation Functions/Layers; Linear Unit; Sigmoid Unit; Softmax Layer; Rectified Linear Unit (ReLU); Hyperbolic Tangent; Neural Network Hands-on with AutoGrad; Summary; Chapter 4: Introduction to Theano; What is Theano; Theano Hands-On; Summary; Chapter 5: Convolutional Neural Networks; Convolution Operation; Pooling Operation; Convolution-Detector-Pooling Building Block; Convolution Variants; Intuition behind CNNs; Summary
  • Chapter 6: Recurrent Neural NetworksRNN Basics; Training RNNs; Bidirectional RNNs; Gradient Explosion and Vanishing; Gradient Clipping; Long Short Term Memory; Summary; Chapter 7: Introduction to Keras; Summary; Chapter 8: Stochastic Gradient Descent; Optimization Problems; Method of Steepest Descent; Batch, Stochastic (Single and Mini-batch) Descent; Batch; Stochastic Single Example; Stochastic Mini-batch; Batch vs. Stochastic; Challenges with SGD; Local Minima; Saddle Points; Selecting the Learning Rate; Slow Progress in Narrow Valleys; Algorithmic Variations on SGD; Momentum
  • Nesterov Accelerated Gradient (NAS)Annealing and Learning Rate Schedules; Adagrad; RMSProp; Adadelta; Adam; Resilient Backpropagation; Equilibrated SGD; Tricks and Tips for using SGD; Preprocessing Input Data; Choice of Activation Function; Preprocessing Target Value; Initializing Parameters; Shuffling Data; Batch Normalization; Early Stopping; Gradient Noise; Parallel and Distributed SGD; Hogwild; Downpour; Hands-on SGD with Downhill; Summary; Chapter 9: Automatic Differentiation; Numerical Differentiation; Symbolic Differentiation; Automatic Differentiation Fundamentals
  • Forward/Tangent Linear ModeReverse/Cotangent/Adjoint Linear Mode; Implementation of Automatic Differentiation; Source Code Transformation; Operator Overloading; Hands-on Automatic Differentiation with Autograd; Summary; Chapter 10: Introduction to GPUs; Summary; Index
Control code
982957880
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781484227664
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-1-4842-2766-4
http://library.link/vocab/ext/overdrive/overdriveId
cl0501000009
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)982957880

Subject

Genre

Member of

Library Locations

    • Ellis LibraryBorrow it
      1020 Lowry Street, Columbia, MO, 65201, US
      38.944491 -92.326012
    • Engineering Library & Technology CommonsBorrow it
      W2001 Lafferre Hall, Columbia, MO, 65211, US
      38.946102 -92.330125

Library Links

Structured data from the Bibframe namespace is licensed under the Creative Commons Attribution 4.0 International License by University of Missouri Libraries. Additional terms may apply to data associated with third party namespaces.
Processing Feedback ...